{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "application_train = pd.read_csv(\"D:\\\\Kaggle\\\\Home_Credit_Default_Risk\\\\clean_data\\\\application_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1713c20e4e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1713c234e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "application_train.loc[(application_train[\"TARGET\"]==1.0), \"AMT_INCOME_TOTAL\"].hist()\n",
    "application_train.loc[(application_train[\"TARGET\"]==0.0), \"AMT_INCOME_TOTAL\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
